a TradingView indicator claims $2,447 in one day and CT is already calling manual trading dead the original post calls it the Claude Fable 5.1 indicator it supposedly watches charts in real time draws projected moves and flags potential entries you still decide whether to take the trade so no this isnt an autonomous bot printing money while you sleep the interesting part is having a second set of eyes on the chart without asking a chatbot to review another screenshot but that $2,447 number needs context starting balance position sizes fees biggest drawdown a trade history longer than one green day the author calls it open source but the post doesnt link the actual code show me the script and the losing days too thats when this gets interesting

your old phone could be more useful than another $1,000 automation course not as a chatbot screen as the device the agent actually operates this Grok Bot demo proposes a simple loop: read screen → identify controls → tap → type → check result the screen reader finds buttons and fields the tapper and typer perform the action the checker looks at what happened before the workflow moves on that last step is the part worth stealing the author says the demo catches a wrong date before it gets sent. without that check, you’re just automating mistakes faster they also report 42 completed tasks while the phone charged from 19% to 100%. that’s a demo claim, not an independently verified benchmark but the build idea is interesting: give an agent a dedicated device start with one app and one repeatable task make it verify the result require approval before anything sensitive gets sent or changed you don’t need to start with “run my entire life” start with the annoying task you already know how to check save this before you forget about that Android sitting in your drawer

what the actual f*ck. one prompt became a six-agent Solana trading desk the author claims they pasted a request into Grok Bot, went to sleep and woke up to: $180 → $4,217 no manually written code, terminal design or agent setup, according to the post the workflow is the part worth unpacking: → SCOUT watches new pairs → FILTER rejects setups that fail the checks → ANALYST scores what survives → EXECUTOR handles approved entries → RISK manages stops and trailing exits → LOGGER records decisions and rejection reasons the reported overnight run: 11,400 pairs scanned 94 passed the filters 41 trades 29 wins / 12 losses largest reported winner: +$1,140 those figures aren’t independently verified. an animated terminal isn’t proof of executable fills or repeatable returns but the build request is specific enough to turn into a paper-trading experiment here’s a version worth testing: “Build a Solana memecoin research and paper-trading desk with six agents: Scout, Filter, Analyst, Executor, Risk and Logger. Scout monitors new pairs on Pump.fun and Raydium using available data sources. Filter checks mint authority, liquidity status, holder concentration and recent volume. Missing evidence must produce a rejection or a flagged review, never an invented pass. Analyst scores only the surviving candidates and explains each score. Executor may paper trade only candidates that passed every required check. Use these as experimental settings, not a validated strategy: reject top-10 concentration above 25% or hourly volume below $40k; test an 8% stop, a trailing rule activated at 15% gain, and a maximum 10% allocation per position. Model fees, slippage and failed fills. Log every entry, exit and rejection with its reason and source timestamp. Build a terminal showing paper balance, agent activity, open positions and equity. Save progress and produce a recap. Do not connect funds or enable live trading without my explicit approval.” save the workflow and test the assumptions the overnight profit claim still needs evidence

what the f*ck is this terminal. the bot hits its target and texts “keep going?” there’s a little creature with eyes watching the trades like it pays rent it’s a Grok Bot concept inside the RUGIKK terminal, with simulated results: $1,500 → $9,087 22 trades 20 wins, 2 losses the interface turns the workflow into messages you can actually follow “skipped $DEVBAG · dev holds 31%” “18K posts on X → buying $XCAT” “skipped $FREEMINT · 41% bundled” “goal 5× done. keep going?” four strategies sit behind it: → GROK SCOUT watches attention on X → SNIPER targets launch entries → CURVE follows the bonding curve → GRADUATE targets the graduation event the more interesting part is what gets rejected the proposed rules skip tokens when the dev holds over 10%, bundles exceed 25%, or a wallet is linked to a previous rug those filters aren’t a safety guarantee. but making the rejection reason visible is a much better interface than another unexplained “buy” alert then the bot pauses at the goal and asks permission to continue that’s the interaction worth stealing clear decisions visible reasons a stopping point the user controls the numbers are simulated, not a verified profit record but a trading interface that explains its skips and knows when to ask is a genuinely interesting build

this Grok Bot terminal claims $44.82 → $11,643.21 overnight the bigger shift is going from “what would you trade?” to reading a log of what the system already did the author says Grok Bot built the terminal without them manually writing the code one screen, six parts: → scanner watches fresh token pairs and volume spikes → rug check screens suspicious tokens before execution → sizer calculates positions from account equity instead of using the same amount every time → executor handles entries, exits and slippage tracking → logger records the reason behind each trade → equity view updates the curve as positions close the point is being able to follow a decision through the system a token appears a check passes or rejects it a position size gets assigned an order gets executed the outcome goes into the log that gives you something to inspect when a trade goes wrong, instead of asking a chatbot to explain itself afterward reported starting balance: $44.82 reported ending balance: $11,643.21 those returns aren’t independently verified. a dashboard and a rising curve don’t establish that the trades happened at the displayed prices but the build idea is worth separating from the profit claim describe the workflow give each component a defined job make the handoffs visible keep the failed trades in the history save this for the architecture the balance still needs evidence

$14.82 → $7,643.21 overnight is a wild claim the more interesting part is the six Grok Bot agents behind the terminal one watches new pairs one checks volume spikes one screens for rug risks one handles position sizing one executes orders one logs the decisions scan → volume → risk check → size → execute → log the dashboard puts the workflow next to the results: → balance and equity curve → enter / skip / wait / exit calls → risk checks before execution → entry prices, exits and slippage → closed-trade stats → a running event log the author reports a 68.3% win rate and 1,412 logged events alongside that overnight balance increase those figures aren’t independently verified. the video also labels its trade-level detail illustrative but the workflow gives builders something concrete to work with a rejected setup should have a reason an order should have a recorded fill a loss should stay in the history and every trade should trace back to the decision that triggered it otherwise you’ve built a convincing animation, not an inspectable trading system 68.3% wins alone won’t tell you whether it works. the size of the losses and the execution costs still matter save the six-agent structure the overnight return still needs proof

this Grok Bot terminal claims it turned $14.82 into $7,643.21 overnight wild number. but the six-agent setup is the part worth looking at instead of asking one chatbot what to buy, the author split the workflow: → scanner finds new pairs → volume agent flags unusual activity → safety agent checks for rug risks → sizing agent decides position size → execution agent handles orders → logging agent records decisions and their reasons the terminal puts the whole process on one screen an equity curve next to the activity feed enter / skip / wait / exit decisions risk checks before execution entry and exit prices with slippage win and loss stats updating as trades close the author reports: starting balance: $14.82 ending balance: $7,643.21 win rate: 68.3% logged events: 1,412 those are self-reported figures, not an independently verified trading record and 68.3% wins doesn’t settle the question. small wins can still get wiped out by a few oversized losses what makes the architecture interesting is being able to trace a trade backward what triggered it which checks passed why that size was chosen what price actually filled what caused the exit that’s much more useful than a green balance with no history behind it if you’re building something similar, make the decisions inspectable before making the profit screenshot impressive save this for the agent split, not the overnight return

give Grok Bot a night shift before you give a trading bot your money before bed, give it one assignment: research a market, propose a testable strategy and build a backtest using historical data ask for fees, slippage and losing periods in the results. keep a separate period for out-of-sample testing wake up with something you can inspect instead of another confident trading prediction then paper trade it before risking anything a bot running 24/7 means nothing if it’s automating a bad strategy

holy f*ck, there’s a Grok Bot template for your vacuum and you’re still writing every prompt from scratch the official use-cases page lists 56 job-focused setups then there’s a marketplace with more bots from the team and community creators some examples worth opening: → Chief of Staff turns email, calendar and notes into a briefing → Expense Manager collects receipts and prepares expense summaries → Outbound Prospecting researches potential customers and drafts messages for approval → Haggle Bot checks SaaS spending for unused seats, duplicate tools and possible savings → Clip Bot turns long recordings into captioned clips → QA bot checks a live deployment against an acceptance checklist → Home robots lets you control supported vacuums and mowers from chat the useful part is starting with a defined job instead of another blank conversation how to try one: 1. open the use-cases page or marketplace 2. choose a task you actually need done 3. click Add and follow the setup 4. connect the required tools 5. give it one small assignment you can check don’t confuse adding a template with having a fully configured employee accounts, integrations and permissions still matter. check your plan and usage costs too use cases: x.ai/bot/use-cases marketplace: x.ai/bot/marketplace save this before spending another evening building a bot that already has a template

holy f**k, Grok Bot has actual jobs and people are still asking it to rewrite emails the useful part isn’t another clever prompt it’s handing over the recurring work that eats your day here’s a list worth saving ↓ your daily admin: → turn email, calendar and notes into a morning briefing → coordinate meetings without chasing every person yourself → research apartments and shortlist the ones that fit → collect the updates that matter instead of reading every channel your business: → research prospects before you contact them → draft outreach and leave it for your approval → prepare calls from previous conversations → keep CRM records and next steps current → organize support work into repeatable routines your money and paperwork: → collect receipts and flag missing expense details → build a weekly expense summary → pull key terms from contracts and surface blocked reviews your product: → reproduce bugs and capture the steps → flag documentation that no longer matches what shipped → monitor cloud-agent runs and report what got stuck → turn performance data into a focused investigation that’s a much better starting point than “run my entire life” pick one task you already understand well enough to check give the bot the right sources, define the finished result and decide what still needs your approval then test it before making it recurring use cases: x.ai/bot/use-cases setup guides: x.ai/bot/guides save this. start with the task you’re tired of doing every week

this is a f*cking goldmine for anyone using Grok Bot 20 public GitHub projects with ready-made bot profiles, specialist teams and workflows you don’t have to build from scratch coding teams with separate reviewers research desks with shared memory sales bots with approval boundaries even a 7-agent trading desk here’s the collection ↓ 1/ awesome-grokbot a searchable catalog of public bot configurations. find a setup for the job instead of spending an afternoon writing another system prompt github.com/kydlikebtc/awesom… 2/ botteams start with a team rather than one overloaded bot. browse company-style setups and their specialist roles github.com/ellelion/botteams 3/ rosterroom 82 paste-ready team rosters. useful when you know what needs doing but haven’t figured out how to split the work github.com/codejunkie99/rost… 4/ grok-bot-profiles separate profiles for specification, implementation and verification the bot writing the code doesn’t get to be its own reviewer github.com/HAEGONG/grok-bot-… 5/ hypergrok-trading-desk 7 agents and 17 skills covering research, position sizing, execution and review a framework for a trading workflow, not proof of a profitable strategy github.com/galleonlabs/hyper… 6/ root-agent-skill-framework a coordinator that decides how many specialists a request needs, creates the bots and delegates management to leads github.com/MrBekoX/root-agen… 7/ awesome-grokbot by mergisi copy-paste profiles, skills and team templates. another place to look before building everything yourself github.com/mergisi/awesome-g… 8/ grok-bot-templates templates, teams, skills and routines with a more structured approach to defining what a bot should actually deliver github.com/cobusgreyling/gro… 9/ botskills paste-ready setups that spell out the action each bot must never take without your approval a useful detail when tools can do more than generate text github.com/PramodDutta/botsk… 10/ grok-skills 195 reusable playbooks. give a bot a process for the task instead of telling it to “be an expert” github.com/jaskirat1616/grok… 11/ crew-contract an operating protocol for bot teams: defined missions, artifact handoffs and completion criteria less ambiguity about who owns what and whether the work is finished github.com/lsj210001/crew-co… 12/ grokbot-for-gtm an outbound GTM playbook connecting prospecting and outreach tools you still need the accounts, integrations and permission to send github.com/bcharleson/grokbo… 13/ gojiberryai-sales-os an AI outbound team powered by the GojiberryAI MCP worth checking if sales research and follow-ups are your bottleneck github.com/romangojiberryAI/… 14/ grok-research a dedicated equity research setup with researchers working under a coordinating role research infrastructure, not a promise of market-beating returns github.com/jaredtrichard/gro… 15/ grok-bot-token-saver a chief-and-workers structure built around shorter role cards and compact handoffs worth exploring before dragging another enormous conversation into the next task github.com/Chakhdz/grok-bot-… 16/ grok-bot-cli create bots and groups, send messages and inspect threads from the command line for workflows that need scripting rather than more manual clicking github.com/ScriptedAlchemy/g… 17/ grok-bot-skill connect a coding agent with Grok Bot teammates so it can message existing bots or create new ones through the CLI github.com/adamanz/grok-bot-… 18/ awesome-grok-bot by majiayu000 another searchable catalog of public bot shares. useful for comparing how different people configure similar jobs github.com/majiayu000/awesom… 19/ akeru-bot a separate local-first alternative with named teammates, per-bot tools, instructions and memory not a Grok Bot template pack github.com/opencoredev/akeru… 20/ guaca a separate agent workspace with shared conversations, persistent memory and MCP tools run it locally or on your own server github.com/madebywelch/guaca don’t install all 20 and call it productivity pick one recurring task read the setup and permissions connect only what it needs check the first result before scheduling anything public code doesn’t mean free execution. subscriptions, APIs, integrations and hosting can still cost money save this before you spend another weekend rebuilding a setup someone already published

this claude code setup is f*cking gold stop paying opus to do sonnet's job let sonnet run the session, give haiku the discovery work, and keep opus available for the decisions that deserve a second opinion start with: claude --model sonnet --advisor opus here's the split to configure: → sonnet owns implementation and edits the code → haiku searches the repo and pulls relevant docs → opus advises on the approach, recurring failures and completion the advisor gets the full conversation when consulted, so you don't have to reconstruct the entire debugging session for it the three checkpoints i'd ask for: 1. before committing to a substantial plan 2. after the same error returns twice 3. before declaring a long task finished sonnet keeps building haiku brings back evidence opus challenges the important decisions the discovery agents should have narrow jobs: search → map the relevant files and modules grep → find callers, references and existing patterns docs → check the actual API documentation all read-only no three agents “helpfully” editing the same file while you're trying to understand it if JEV is already wired into your stack, keep it on deterministic routing where it helps. it isn't required to use the advisor paste this into claude code: “Propose this setup for my existing configuration: 1. Sonnet as the main model, with Opus as advisor. 2. Three read-only Haiku subagents: search for repository mapping, grep for call sites, and docs for primary API references. 3. Add instructions to consult the advisor before substantial plans, after the same error twice, and before completing long tasks. 4. Inspect existing agents first. Reuse suitable ones and flag conflicting settings. 5. If JEV is installed, show where deterministic routing could use it. Otherwise leave it out. Show every proposed change as a diff. Do not edit anything until I approve.” one catch: advisor calls still consume tokens, and consultation timing is model-driven rather than a hard three-call limit use it where a bad decision would waste more work than the review costs save this for your next refactor

the boring grok bot idea is the one i'd actually test not another “ai startup” one narrow directory that answers a question people already search for think niche software comparisons, local services or a useful industry database here's the setup: → create a bot and call it Mogul → have it research recurring complaints and unanswered questions in one niche → turn those findings into a small directory with sources, useful filters and clear comparisons → draft one genuinely useful page at a time → review the pages before publishing → save the repeatable work as a routine → track visits, outbound clicks and actual conversions the interesting part is the maintenance a directory gets worse when prices change, links die and listings go stale that's work a bot can help check on a schedule instead of leaving the site untouched after launch monetization comes later affiliate links where relevant paid listings clearly labeled sponsorships that don't secretly buy the rankings have the bot draft outreach, then approve what gets sent your laptop doesn't need to stay open for a cloud routine to run but don't confuse that with a business that runs itself hosting, usage limits, editorial checks and getting anyone to visit still matter 30 generated pages aren't an asset just because they exist 30 useful pages that answer specific questions are at least something you can test start with one niche and five strong pages watch what people actually use then decide whether the next 25 deserve to be built

this claude setup is getting f*cking dangerous give it persistent notes, access to your workspace and browser tools, and the chat starts looking a lot more like an operating desk here's the build: 1. create a folder called SECOND BRAIN open claude code inside it. keep your notes and project context somewhere you can actually inspect 2. add a MEMORY subfolder use it for decisions, preferences, project status and unfinished work not another giant chat transcript nobody will read 3. create CLAUDE.md in the root tell claude where information belongs, what to read before starting, and what to save when a task ends the files carry context between sessions the instructions tell it how to use them 4. connect google workspace through GWS CLI authorize the Drive, Gmail and Calendar access your workflow actually needs start read-only. your entire inbox doesn't need to become an experiment 5. add Graphify if your notes need connections a knowledge graph can help surface relationships across files instead of leaving every useful detail buried in a separate document 6. connect Playwright MCP for browser tasks now the workflow can include navigating pages and interacting with web interfaces, within the permissions you allow 7. package a specific routine as a skill for example: read today's calendar find the relevant project notes prepare a meeting brief save the summary back into MEMORY one repeatable workflow instead of rebuilding the same prompt every morning keep approval required for sending emails, deleting files or changing external data and don't confuse a folder called MEMORY with perfect recall the useful part is having context you can inspect, correct and reuse start with one routine that saves you time then expand the system

your opus context is too expensive for this sh*t stop filling it with transcripts from agents doing repetitive work claude code workflows let a script coordinate the run instead of making the main model babysit every step here's the setup i'd use: → opus designs the workflow → haiku handles read-only discovery → sonnet makes scoped changes → a separate verifier checks the evidence → opus reviews what actually survived the model split is something you configure, not a default to assume start here: claude --model opus --effort ultracode the important change isn't “spawn more agents” it's moving the execution loop into code the workflow holds the intermediate results the main session gets the useful outcome instead of every worker's running commentary for a large repo, structure the work like this: 1. discover the files in scope 2. scan them for a specific class of issue 3. collect findings with paths and evidence 4. reject duplicates and unsupported claims 5. patch confirmed issues in isolated worktrees 6. independently verify the changes 7. review the final diff before committing opus shouldn't need to reread 50 conversations to decide whether one patch is ready and if JEV is already part of your stack, keep it on the lightweight routing decisions rather than pretending every branch needs heavyweight reasoning paste this into claude code: “Propose a reusable workflow for this repository. Use Opus for architecture and final review, Haiku for read-only discovery, and Sonnet for edits where available. Give editing agents separate worktrees and clearly bounded file ownership. Require an independent verifier for every reported issue and proposed fix. Stop correction loops after two rounds without progress. Report unresolved failures instead of hiding them. Begin with one directory and report usage before expanding. Show the proposed workflow and configuration changes as diffs first. Do not edit or commit anything until I approve.” one thing worth remembering: cleaner context does not mean free compute parallel workers still consume tokens, so test the split on a small task before pointing it at the whole repo save this for the next audit or migration that would otherwise drown your main session in logs

this claude code setup is a f*cking goldmine stop making your most expensive model babysit every file in the repo give it the architecture let cheaper agents handle the repetitive work the setup: → opus writes the workflow → haiku handles read-only scans → sonnet handles fixes → a separate verifier checks the results that model split is the configuration to ask for, not something to assume is already enabled the important part is where the loop lives instead of dumping every worker conversation into the main chat, put the orchestration in a reusable script the script coordinates the work opus gets the findings that actually need its attention start with: claude --model opus --effort ultracode then describe the task and the model split you want for a repo-wide cleanup, i'd structure it like this: 1. scan a small directory first 2. collect findings with file paths and evidence 3. reject duplicates and unsupported claims 4. give each editor an isolated worktree 5. verify the patches independently 6. bring the surviving results back for review opus plans haiku searches sonnet patches and none of them gets to approve its own homework the JEV layer is optional: if you already have it wired in, use it for the routing decisions that don't need another heavyweight reasoning call paste this into claude code: “Design a reusable workflow for this repository. Use Opus for planning and final review, Haiku for read-only discovery, and Sonnet for edits where those models are available. Keep editing agents in separate worktrees. Require evidence for every finding and an independent verification pass for every proposed fix. Stop fix loops after two rounds without progress. Start with one directory so we can inspect quality and usage before scaling up. Show the workflow and proposed configuration changes first. Do not edit files or commit anything until I approve.” one catch: parallel agents still consume tokens less clutter in the main context does not mean free compute save the expensive reasoning for decisions that deserve it

claude can write your trading bot. the dangerous part is letting it trade without knowing when to stop. this shared blueprint is more interesting than another “paste a prompt and get rich” demo. it describes a system with four layers: → market regime detection identify whether conditions are quiet, ranging, volatile or falling sharply, then select the appropriate strategy → risk controls check a kill switch before every order and stop trading at a configured drawdown threshold, with 15% used in the example → testing before deployment evaluate the strategy across successive historical periods and keep it out of live trading if it fails the acceptance criteria → overnight review analyse failures and prepare proposed improvements, with a human deciding whether they get applied that’s a much better starting point than running the same strategy through every market. but the “one minute, no code, just paste” pitch skips the hard part. generated code still needs inspection. backtests can overfit. a 15% drawdown setting cannot guarantee losses stop at exactly 15%. let claude help build the system. test it in paper trading before giving it real money. a bot that knows how to place an order is easy to demo. proving when it should NOT place one is the actual work.

$508k PnL on Polymarket. the claimed edge is a straight line and a noise filter. Daniro credits a Grok Bot-built system with +$508,183 over 125 days. that averages roughly $4,065 a day across the period, not a guaranteed daily payout. the reported setup targets crypto “Up or Down” markets: → around $258 per average trade → a claimed frequency of 33 trades an hour → regression to measure direction → R² to check how closely prices follow that trend here’s the actual idea. 1. measure the move fit a line through recent BTC or ETH prices: slope = Cov(time, price) / Var(time) positive slope suggests upward momentum. negative slope suggests downward momentum. the magnitude measures how quickly price is moving over the chosen window. 2. check the fit R² = 1 − SSE / SST a higher R² means the line explains more of the price variation in that sample. a lower R² means price is moving less consistently around the trend. the described rule combines directional momentum with a stronger fit before taking a trade. the account named in the post is mo-money. listed winning examples: $1,666 → $4,175 $1,120 → $3,556 $587 → $2,609 interesting as f*ck, but the missing details matter. the post doesn’t establish that this exact algorithm produced the account’s results. a high R² describes past data, not the probability of winning the next market. entry odds, latency, fees and losing trades still decide whether the idea actually makes money.

stop asking AI to draw monsters. make it build their bones first. this blender workflow is f*cking interesting because it starts with the part nobody puts in the thumbnail: the skeleton. the shared setup describes three stages: → FRAME define the limbs, joints and balance before creating the mesh → FLESH build muscle and body volume around that structure → WARP distort the working anatomy to make the creature look unnatural that order matters. a creature can look incredible in an image while giving you a nightmare when you try to rig it. cool silhouette, joints that make no sense. starting with the skeleton gives the design something functional to deform. lengthen a pair of limbs. change the curve of the spine. shift the proportions. you’re modifying an existing structure instead of asking the model to invent a completely new body every time. and because the frame is parametric, variations can come from changing values rather than rebuilding everything. the underrated detail is weight distribution. it affects how the creature stands, which changes whether it reads as powerful, unstable or barely alive. you still need to check the rig and motion. a procedural skeleton doesn’t magically guarantee good animation. but “build something that can move, then make it weird” is a much better brief than “generate a terrifying monster.”